We considered a class of Gierer-Meinhardt models without  diffusion term. Firstly, we studied the existence,  the stability of equilibrium points and the various bifurcation phenomena of the model. Secondly, by choosing proper bifurcation parameters, we proved that the system had saddle-node bifurcation, Hopf bifurcation and Bogdanov-Takens bifurcation of codimensions 2. Finally, the theoretical results were demonstrated by numerical simulations. The results show that the rate μ influences the instability of system.

By using  the critical point theory, we prove the existence of the non-trivial solution of the finite difference scheme for the three-point boundary value problem and get the existence result of the numerical correlation solution for the above continuous problem.

Firstly, we used the potential well theory, the inverse Sobolev inequality, Fountain’s theorem and other tools to discuss  the blow-up problem of the solution to a class of pseudo-parabolic equations with variable exponential logarithmic nonlocal terms and singular potentials, and obtained the results of the solution of the problem to blow-up in finite time at arbitrarily high initial energy levels. Secondly, by combining  the Gagliardo-Nirenberg interpolation inequality and  Sobolev embedding method, and by constructing auxiliary functions, we gave the upper and lower bounds estimates for  the blow-up time of the solutions to the problem  under appropriate conditions.

By using  the weak convergence methods for nonlinear partial differential equations (PDEs), the author proved the existence of solutions to a class of quasi-linear elliptic equations with gradient term and zero-order term. The main characteristic of the equation was  that the coefficient function of the gradient term b∈L^N(Ω), but its  norm ‖b‖_N,Ω was not required to be sufficiently 
 small. Firstly, by segmenting the bounded domain Ω, the solution sequence {u^t}_0<t<1 was  split into a sum of some subfunctions, and the energy estimate of the subfunction was limited to small  subdomain. Secondly, the author obtained the energy estimate of  {u^t}_0<t<1 on W^1,p₀(Ω) by using  iterative techniques. Finally, with the help of Boccardo-Murat’s technique, the author proved the almost everywhere convergence of the gradient solution sequence {u^t}_0<t<1, and determined the convergence element  of the nonlinear term of the equation based on this convergence.

We considered  the existence of pullback attractors in non-autonomous wave equations. Firstly, we proved the well-posedness of its solution  according to the uniform sector operator theory. Secondly, the bounded dissipation of the process was obtained by constructing an appropriate functional. Finally, the asymptotic compactness of the process was verified according to the 
operator decomposition technique, and ultimately the existence of the pullback attractor was obtained.  This study not only considers time-dependent damping, but also time-dependent diffusion and time-dependent nonlinear terms. When these time-dependent coefficients meet the appropriate conditions, the existence of the pullback attractor is proven.

We considered the initial (boundary) value problem for the two-dimensional  chemotaxis-shallow water  model with vacuum, and  established a new blow up criteria for the local strong solutions of the model by using the energy method and some important inequalities. We also gave  some new factors that  affected the extension of strong solutions.

By using Leray-Schauder fixed point theorem, we study a class of fully fourth-order nonlinear ordinary differential equations. The existence and uniqueness of the positive solutions are proven under the condition that the nonlinear term f grows at most linearly. Under the condition that f satisfies the superlinear growth, the existence of positive solutions are obtained by introducing a Nagumo-type condition to limit that f(t,x₀,x₁,x₂,x₃) is quadratical growth on x₃ at most.

Firstly, we established and discussed a four-dimensional ecoepidemiological model in which both prey and predator populations were infected, and the infected population had self-recovery ability. Secondly, we established the corresponding optimal control model and  used Pontryagin’s maximum principle to calculate its optimal control strategy. Finally, we investigated the dynamic behaviors of the model and its optimal control strategy by using numerical analysis. The results show that the recovery rate of diseases has a significant impact on the dynamic behavior of the system.

By using Leray-Schauder degree theory and method of upper and lower solutions, the author  study the existence and multiplicity of positive solutions for the elastic beam equation with nonhomogeneous boundary conditions.  When the nonlinear term f satisfies suitable conditions, the author prove that there is a positive number b*, such that there are at least two positive solutions to the problem when 0<b<b*, there is exactly one positive solution to the problem when b=b*, there is no positive solution to the problem when b>b*.

We considered the blow-up phenomena of solutions to  a class of fourth-order parabolic equations. Firstly, we  introduced a new inequality. Secondly, under appropriate conditions, we  proved that the solution to the equation blew up in finite time and gave an upper bound estimate for the blow-up time. Finally, using the new inequality and differential inequality techniques, we obtained a lower bound estimate for the blow-up time of the solution.

We considered the existence of radial solutions to Dirichlet problems for a class of p-Monge-Ampère systems with multiparameter. By utilizing the fixed point index theorem on cones, we prove the existence results of radial solutions when the parameters are large enough and nonlinear terms satisfy  appropriate growth conditions.

Let H be a Hilbert space over the real or complex field F, N be a non-trivial nest on H and τ(N) be the associated nest algebra. Suppose s: τ(N)→τ(N) is a map without additivity and linearity. With the help of method of algebratic decomposition, we prove that if 3s(X^.Y^.Z)=s(X)^.Y^.Z+X^.s(Y)^. Z+X^.Y^.sZ) for any X,Y,Z∈τ(N ) with XYZ=0, then s is an additive centralizer.

By using the function decomposition and the inequality technique, with the aid of the theory of boundedness for generalized fractional integral operator commutators on the L^p spaces, the author discussed the boundedness of the commutator T_σ,b generated by the generalized fractional integral operator T_σ and the Lipschitz function b on non-homogeneous metric measure spaces. The results show  that the  T_σ,b is bounded from Morrey spaces M^p1_q1(μ) to M^p2_q2(μ), and also bounded from Morrey spaces M^p_q(μ) to RBMO(μ) spaces.

We studied the stability of a class of hyperbolic systems with balance laws under delayed feedback control and the spectral analysis of the system operator  by using the strictly weighted Lyapunov function method and asymptotic analysis technique. Firstly, the well-posedness of the systems was verified by using operator semigroup theory. Secondly, the exponential stability of the closed-loop system was analyzed by constructing a strictly weighted Lyapunov function. Finally, according to  the asymptotic analysis technique, the asymptotic expressions of eigenvalues and eigenfunctions were obtained. The result shows that the system is exponentially stable when the feedback parameters and delayed value satisfy certain inequality constraints, and when the modulus of eigenvalues tends to positive infinity, the real part of the eigenvalues tends to a negative constant.

Firstly, aiming at the problem of traditional single-stage remote sensing image fusion task that required a large number of supervised samples and poor retention of image feature information, we proposed a two-stage panchromatic sharpening method for remote sensing images. The method achieved the fusion of remote sensing images by decomposing the task into two tasks of feature fusion and super-resolution. In the first stage,  the adversarial network feature fusion was generated, and in the second stage,  the super-resolution network generated clearer spatial features,  achieving the goal of high quality remote sensing image fusion. Secondly, the  multiple experiments were conducted by using GaoFen-2 and WorldView-3 satellite datasets to verify the effectiveness of the proposed method, and the fusion results were evaluated by using reference image quality indexes and non-reference image quality indexes, respectively. The experimental results show that the method can better retain the spectral feature information and spatial feature details compared to the traditional methods, and effectively improving the visual effect of the fused image.

Aiming at the limitations of extracting retinal features from single-mode retinal images, we proposed a multi-modal retinal disease diagnosis model based on multi-level and multi-scale attention fusion network. Firstly, the multi-level attention network and multi-scale attention network were designed for color retinal images  and retinal optical coherence tomography respectively, and the fusion features were obtained by merging at the feature layer. Secondly, the weighted loss function of the two modes and the loss function of the fusion features were added to extract the unique and complementary information of the two modes in order to  improve the accuracy of retinal disease diagnosis. The results of evaluation experiments  on the MMC-AMD dataset and GAMMA dataset show that the proposed model outperforms  the current mainstream models and has superior diagnostic effect.

Based on an improved  spatial pyramid attention module, we  enhanced the performance of lightweight networks in fine-grained image classification tasks. By combining global and local features, the  improved model enhanced the classification performance of lightweight networks without significantly increasing the number of parameters. The experimental results  on the Stanford Dogs dataset show  that the lightweight network equipped with this module significantly improves accuracy, even surpassing some classical models. This method expands the application scope of lightweight networks on resource-constrained devices and provides an efficient and low-computational-cost solution for fine-grained image classification problems.

Aiming at  the problem of poor effect of the style transfer of existing image ink painting, we proposed a new data enhanced cycle generative adversarial network (GAN) for the style transfer of ink painting of unpaired natural landscape photos. Firstly, the binary synthesizer and discriminator structure was designed to effectively improve the mapping constraints of one-way GAN models. Secondly, we used multiple loss functions to optimize the model, introduced total variational loss and identity mapping loss, and designed a new cyclic consistency loss function combined with multi-scale structural similarity to better capture the characteristics of traditional ink painting. Finally, data enhancement techniques were used to increase the amount and variety of real and generated data to improve generator performance. The comparative experimental results show that this method can effectively transfer natural landscape images to traditional ink painting style images.

Aming at the problems  of diversity and poor image quality in agricultural disease image datasets, we  proposed a multisource agricultural disease image dataset construction method based on hierarchical annotation and adaptive preprocessing. Firstly, images were collected from different regions, crop types, and growth stages by using devices such as smartphones, professional cameras, and drones to ensure data diversity. Secondly, we constructed a hierarchical annotation system that  covered three levels of agricultural disease type, severity, and location, we used tools such as LabelImg and LabelMe for annotation, and requested expert review. Finally, we applied adaptive preprocessing methods, including automatic cropping, normalization, denoising and enhancement, to adjust parameters based on image features to improve quality. The experiment used a convolutional neural network (CNN) model based on the ResNet-50 architecture for validation, and the results show that hierarchical annotation and adaptive preprocessing methods significantly improve the quality of the dataset and model performance, the model achieves accuracy, recall, and F₁ score of 92.5%, 91.8%, and 92.1%, respectively, which are better than the training results of other datasets.

Aming at the problem of  the computational complexity caused by the exponential increase in the number of partitioning schemes with the number of vertices in large-scale graph partitioning problems, and the inefficiency and imprecision of traditional genetic algorithms when dealing with large-scale problems, we proposed a hybrid genetic algorithm. Firstly, the algorithm  performed optimal matching on individuals encoded in binary, identified and screened out good genes,  effectively narrowing down the search range and focusing on more promising search areas. Secondly, in order to avoid the illegal solutions that might arise from traditional crossover operations, the algorithm abandoned the random crossover strategy and only generated one potential solution. Finally, by introducing a tabu search operator in the mutation operation to generate complete individuals, thereby enhancing  local search capability of the algorithm and achieving a dynamic balance between global and local searches. Experimental results of applying this hybrid genetic algorithm to the partitioning problem of ultra large-scale integrated circuits show that the algorithm can effectively improve the quality of solutions for large-scale graph partitioning problems.

Aiming at  the problem of uneven initial solution distribution in the traditional gray wolf optimization algorithm, we proposed an enhanced gray wolf optimization (EGWO) algorithm. Firstly, we introduced nonlinear convergence factors to improve gray wolf optimizaiton algorithm. Secondly,  the Sobel sequence was integrated into the improved gray wolf optimization algorithm to increase the population diversity. In order to verify the effectiveness of the proposed algorithm, EGWO algorithm was applied to UAV path planning, and compared with the traditional gray wolf optimization algorithm based on multiple evaluation indicators. Experimental results show that the EGWO algorithm has better performance, and  can quickly and accurately plan and control the flight path of UAVs in complex environments, as well as improve the flight efficiency of UAVs in swarm control.

Aiming at the problems of low recognition accuracy and poor sensitivity of Googlenet model, we proposed a hyperparameter optimization Googlenet model by using  the improved sea-horse  optimization (SASHO) algorithm.  Firstly, the sea-horse optimization algorithm was improved by using Sobel sequence and adaptive weight algorithm. Secondly, the four basic neural networks were compared to select the most suitable Googlenet for this dataset as the basic recognition model. Finally, the improved SASHO algorithm was used to optimize the parameters of Googlenet model, and a new model SASHO-Googlenet was constructed. In order to verify the effectiveness of SASHO-Googlenet model, the SASHO-Googlenet model was compared with the model optimized by the other four swarm intelligence algorithms for seven indicators. The results show that the accuracy rate of SASHO-Googlenet model is 95.36%, the sensitivity is 95.35%, the specificity is 95.39%, the accuracy is 96.47%, the recall rat
e is 95.35%, the f_measure is 95.90%, and the g_mean is 95.37%. Experimental results show that the SASHO-Googlenet  model has the best comprehensive performance.

Aiming at the problem that it was difficult to balance the diversity and convergence of multi-objective particle swarm optimization algorithms, we proposed a dynamic population-based multi-objective particle swarm optimization algorithm. The increase or decrease of the population size of this algorithm depended on the resources in the archive, thereby  regulating the population size. On the one hand, particles were added by local perturbation based on grid technology to increase the local search ability of particles and improve the diversity of the algorithm. On the other hand, in order to prevent the population size from overgrowing, non-dominated ordering and population density were used to control the population size and  accelerate the algorithm search progress, avoiding premature convergence. Five comparative algorithms were selected for experiments on test functions, and the experimental results show that this algorithm has obvious diversity and convergence advantages.

Aiming at the problem of poor fusion performance in traditional multimodal data feature fusion algorithms, the author proposed a
 multimodal data feature fusion algorithm based on deep learning and D-S theory.  Firstly, within the framework of deep learning, a restricted Boltzmann machine (RBM) was used to train multimodal data. Based on the characteristics of the data and task requirements, an RBM model structure was constructed for multimodal data feature selection. Secondly, based on the selected features, the author calculated the distance between similar modal data, determined the trust function, and set a threshold to remove abnormal data, achieving preliminary fusion of similar modal data. Finally, by calculating the distance between heterogeneous modal data and feartures of different levels, the author determined the trust function of heterogeneous data, and combined with D-S theory, multimodal data feature fusion was achieved. The experimental results show that the purity of the proposed algorithm can reach up to 1.0, and the standardized mutual information can reach up to 0.3, indicating that the proposed algorithm can obtain accurate multimodal data feature fusion results.

Aiming at the problem of low-quality initial solutions and insufficient diversity, we proposed a random forest model that introduced the Logistic chaos mapping to improve the optimization of the sea horse optimization algorithm. Firstly, after improving the sea horse optimization algorithm, it was combined with the random forest algorithm to improve the discriminative accuracy of the classic random forest algorithm. Secondly, in order to  verify the  performance of new model, comparative experiment  was conducted by using  five models for four evaluation metrics. The experimental results show that the model has accuracy rate of  96.15%,  precision of 100%, recall rate of 92.31%, and  F₁-Score of 96.00%, which improves the performance of the  random forest method.

Aiming at the problems of oversized models, slow detection speeds, and high complexity in existing unmanned aerial vehicle (UAN)  target detection algorithms, we  proposed an improved lightweight UAN  detection algorithm YOLO-LDD based on YOLOv5n.  Firstly, on the basis of YOLOv5n, a diversified branch  module DBB and C3 module were introduced to  fuse and  reconstruct into  C3_DBB module, enhancing the representational capacity of individual convolutions. Secondly, a reparameterized structure convolution RepConv was introduced into the neck network to improve detection speed. Finally, the model was compressed by using the layer-adaptive magnitude-based pruning (LAMP) method to reduce the number of parameters. Experimental results show  that the proposed algorithm can maintain excellent detection performance while reducing computational and storage demands, and improve  efficiency and inference speed of the model. The  average accuracy reaches  96.7%, the  parameter count is reduced by 73% compared to YOLOv5n, the computational load is reduced by 60%,  and a detection speed is increased by  1.6 times.

Aiming at the problem of dynamicity of  the wireless local area networks (LAN), which led to the changes of the validity of data labels with time, and required regular  updates and relabeling of data,  increasing the difficulty of data label labeling, we proposed  an adaptive labeling method for multimodal data labels in  wireless local area networks. Firstly, the repetitive wireless LAN multimodal data was cleaned by using dynamic sliding neighbor sorting algorithm, and the multimodal data was fused by using recurrent  neural network to obtain more comprehensive data information. Secondly, the fused wireless LAN  data was divided into deterministic set and fuzzy set, and the deterministic set data was labeled by using support vector machine, and the fuzzy set data was labeled by using K-nearest neighbor (KNN) classifier, thus achieving  the adaptive labeling of wireless LAN multimodal data labels. The experimental results show that the deduplication ratio of the proposed method is always above 12%, the consistency index is 0.992 8, the average absolute percentage error is 0.453 9, the ROC curve is closer to the upper left corner of the coordinate axis, the AUC value is 0.982 4, and the memory occupancy rate is always below 10%. The wireless LAN multimodal data labeling effect is good.

Aiming at the problems of poor learning performance and high fluctuation in the deep deterministic policy gradient (DDPG) algorithm for tasks with some large state spaces, we proposed a multi-actor deep deterministic policy gradient algorithm based on progressive k-means clustering (MDDPG-PK-Means) algorithm. In the training process, when selecting actions for the state at each time step, the decision-making of the actor network was  assisted based on the discrimination results of  the k-means clustering algorithm. At the same time, as the training steps increased, the number of k-means cluster centers gradually increased. The MDDPG-PK-Means algorithm was applied to the MuJoCo simulation platform, the experimental results show that, compared with 
 DDPG and other algorithms, the MDDPG-PK-Means algorithm has better performance  in most continuous tasks.

The author investigated the thermal convection flow of Oldroyd-B fluids in a fluid-porous medium two-layer system heated from below, and analyzed the effects of viscoelastic properties of the Oldroyd-B fluid on oscillatory instabilities and its dominant branches under a vertical magnetic field. It was assumed that the flow in the porous medium was controlled by a modified Darcy’s law, and the boundaries were subject to isothermal conditions. Linear stability analysis of the control equations was conducted, and neutral curves for different parameters were obtained by using the Chebyshev collocation method. The results show that the effects of the thickness ratio and Chandrasekhar number on convective stability are opposite, and increasing the thickness ratio can gradually diminish the bimodal characteristics of the neutral curve. Additionally, as the magnetic field intensity increases, the start time of oscillatory convection is delayed, and the convective dominant branch shifts from short-wave branch to long-wave branch. After the conversion, the effect of magnetic field intensity on thermal convection stability is relatively small.

Firstly, we gave the definition of function matrix projection synchronization. Secondly, by using the active control method, the function matrix projection synchronization was realized between two disturbed fractional-order motor chaotic systems, and the effectiveness of the controller was demonstrated by using two methods. Finally, the correctness and effectiveness of the synchronization theoretical analysis were verified by MATLAB numerical simulations.

On the basis of the FitzHugh-Nagumo(FHN) neuron model containing phototubes, we connected the inductors in series with phototubes to construct a photosensitive neuron model. Firstly, we plotted the time-space diagram and membrane potential phase diagram of the network under different coupling strengths by numerical simulation. Secondly, we changed the amplitude of the external excitation source of the neuron and the output voltage amplitude of the phototube separately, and calculated the order parameter SI under the corresponding parameters. Finally, the values of the amplitude of the external excitation source and the output voltage amplitude of the phototube for each neuron were randomly selected from a normal distribution, we constructed a non-all-identical neural network, changed the coupling strength and the standard deviation of the normal distribution, and calculated the corresponding order parameter SI. The results show that, when the coupling strength increases within a certain parameter range, the network can transition from asynchronous to chimera state, and ultimately achieving synchronous. The appropriate value of amplitude can expand the coupling strength interval corresponding to chimera state. In the non-all-identical neuron network, the coupling strength interval corresponding to the chimera state is positively correlated with the value of the standard deviation.

Aiming at the problem that the estimation accuracy of the existing estimation methods of the direction of arrival (DOA) was low under the condition of low signal-to-noise ratio, small fast beat and multiple sources, we proposed a DOA estimation method based on parallel coordinate descent algorithm. Firstly, the airspace was uniformly divided into equal angles, and the super-complete redundant dictionary was constructed. Secondly, the sparse signal was reconstructed by using the idea of parallel coordinate descent algorithm, and the sparse coefficient matrix of the signal in spatial space was obtained. Finally, the l₂-norm of the sparse matrix row vector was mapped to the spatial grid to obtain an accurate DOA estimate. The simulation experiment results show that the proposed method is superior to subspace algorithm, greedy algorithm and convex optimization algorithm under the conditions of low signal-to-noise ratio, small fast beat and multiple sources, and has lower root mean square error (RMSE), higher DOA estimation accuracy and higher operational efficiency.

We designed and synthesized  a crotonyloxy-substituted zinc porphyrin (TMPTPPZn) complex， which was copolymerized with methyl methacrylate (MMA) to obtain a zinc porphyrin/polymethyl methacrylate (PMMA) copolymer (TMPTPPZn-PMMA). Then using polyacrylonitrile (PAN) as the spinning solution, we prepared composite fibers TMPTPPZn-PMMA/AgX/PAN loaded with TMPTPPZn-PMMA and AgX(X=Cl,Br,I) by using electrospinning technology. The morphology of the composite fibers was studied by scanning electron microscopy (SEM),  the thermal stability of the composite fibers was studied by using thermogravimetric analysis (TGA), and studying their catalytic activity for the degradation of organic dyes. The results show that the TMPTPPZn-PMMA/AgCl/PAN,  TMPTPPZn-PMMA/AgBr/PAN and TMPTPPZn-PMMA/AgI/PAN all have high photocatalytic activity and 
can effectively catalyze the degradation of organic dyes in wastewater. After 240 min of visible light irradiation,  the degradation rates of methylene blue (MB) by the three composite fibers are  96.02%,94.75% and 90.91%,  and the degradation rates of rhodamine B (RhB) are 93.40%,94.71% and 96.54%,  respectively.

The adsorption mechanism of inert gases CH₄，N₂ and CO₂ on coal surface was analyzed by using density functional theory (DFT) M062X method. According to the Millikan charge distribution of each atom in the  coal molecules,  adsorption vacancies on the surface of coal molecules were determined. The adsorption models of CH₄，N₂ and CO₂ as single component gases  on the coal surface were established on each adsorption vacancy. By analyzing and comparing the microscopic parameters such as bond length,  bond angle,  adsorption distance and adsorption energy of each adsorption model,  the author obtains that  the affinity order of the three kinds of gas adsored on coal surface is  CO₂>CH₄>N₂, and reveals  the microscopic states of CH₄，N₂ and CO₂ adsorption on coal surface.

Rana dybowskii residue was used as raw material， the  Rana dybowskii residue peptides (RDRP) was abtained  under enzymatic hydrolysis conditions, and then  were chelated with selenium ions (Se⁴⁺) to prepare  Rana dybowskii residue peptide chelated selenium (RDRPCS). We characterized the synthesized compounds, studied the optimized conditions for chelation reaction, and investigated their anti-inflammatory activity through in vitro cell models. The experimental results show that  Se⁴⁺ reacts with RDRP to form chelates.  RDRPCS has sufficient metal binding sites,  with chelating sites being  amino,  hydroxyl,  and C=O.    The optimization result of chelation process is that temperature is 60  ℃,   pH=6,  time is 65 min,  and a mass ratio is 2.5∶1， the efficiency of chelation reaction under this conditions is 70.7%.  Different concentrations of chelates have inhibitory effects on the release of  NO, TNF-α,IL-6 and IL-1β in RAW264.7 inflammatory cell model.

In order to realize the comprehensive reuse of solid waste,  the iron/sludge-based biochar catalyst  was prepared by using the mixed sludge of activated sludge and Fenton iron sludge from the sewage treatment section of steel plant in Anshan, Liaoning Province as raw materials， and kaolinite as binder by a mixing-extrusion and pyrolysis method. The structure was characterized and the preparation process conditions were optimized. We optimized the process parameters of catalytic ozonation degradation of simulated wastewater containing quinoline as the target pollutant, and studied the stability and catalytic mechanism of catalyst. The experimental results show that the surface of catalyst is rough with a rich porous structures, and the catalyst mainly contains Fe₃O₄，CaCO₃ and ZrO₂ crystals. The optimal preparation conditions for the catalyst are a binder addition of 12.5%, pyrolysis temperature of 800 ℃, and pyrolysis time of 3 h. Under the conditions of initial pH=7, catalyst dosage of 50 g,  and  initial mass concentration of 50 mg/L of quinoline, the removal rate reaches 76.31% after 10 min of reaction. The catalyst has good stability,  and the removal rate of quinoline only decreases by 0.09 percentage points  after continuous  use for five times. There are two types of   hydroxyl radical (^.OH) and superoxide anion radical (O^.-₂), as well as singlet oxygen  (¹O₂) non  free radicals  in the catalytic system, with  O^.-₂   playing a dominant role in the reaction. The change of Fe²⁺/Fe³⁺ ratio before and after the reaction indicates that the redox cycle of Fe²⁺/Fe³⁺ participates in the catalytic degradation reaction. This study provides a new idea and method for the reuse of solid waste and the efficient treatment of coking wastewater.